Cognitive computing, artificial intelligence or augmented intelligence, as well as machine learning, play currently a significant role in computer research and in practical implications. Such systems are predestinated to support humans in dealing with overwhelming amounts of data to make quick and solid decision, and this also in light in light of contradictory information details. Often, question answering (QA) technologies are applied to support such decision-making. Question answering technologies typically taken input question, analyze these, and return results indicative of the most probable answer to the input questions based on the knowledge stored in such system. Typically, the knowledge stored in data graphs is used as a basis. Data or facts are very often organized as nodes and relationships between them. Such knowledge graphs cover a wide range of applications, such as chemical, bio informatics, computer vision, social networks, text retrieval, web analysis, just to name a few. One of the characteristics of such knowledge graphs is that they can grow exponentially over time, become more and more complex and require more and more storage capacity. Mining these partially over-complex and sometimes outdated subtrees of knowledge graphs becomes very time and resource consuming such that it becomes more and more difficult to get quick responses to queries against these overloaded knowledge graphs.